Claude Skills by JoaoEquer

Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.

Turn scope documents (data model/UML, functional proposal, execution plan) into structured ClickUp tasks — modules as parent tasks, subtasks with "how to" descriptions, dependencies and CSV import. Use whenever you need to create tasks, break scope into activities, organize a backlog, plan a sprint or structure an execution plan in a project management tool.

Writing client-facing technical documentation — architecture documents, proposals, project presentations — in plain language, jargon-free, with honest numbers and explicit pragmatic engineering principles. Use whenever the material will be read by a client or non-technical stakeholder - architecture doc, kickoff presentation, proposal, delivery report or closing email.

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

Build and sharpen a project's domain model. Use when discussing codebase terminology, writing or editing a CONTEXT.md, or recording or editing an ADR.

House pattern for Express + Prisma backends structured as manual Clean Architecture (controller → usecase → repository, wired by hand in a factory) — the real shape of dream-book-api and simple-management-api. Use whenever creating a new route, usecase or domain in an Express backend on this stack, even if the request is just "create the X endpoint" or "add the Y usecase".

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

House pattern for building complete CRUD domains in NestJS with Prisma — controller/service/repository with dependency inversion, validated DTOs and multi-tenant isolation. DEPRECATED — no active Wibi backend uses NestJS today; use only if a project explicitly picks NestJS. For the real production stack (Express), use express-prisma-pattern instead.

Data modeling conventions for Prisma + PostgreSQL in multi-tenant systems — workspace isolation, numeric criticality, idempotent recurrence, document versioning by lineage and decoupled audit via a queue in Postgres itself. Use whenever creating or changing schema.prisma, designing new entities, planning migrations or discussing data modeling in any project on this stack.

Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).

Role-based access control design with roles + granular permissions — role × domain matrix, N:N database modeling and an approval process before implementation. Use whenever the project involves permissions, user roles, access control, admin panels or "who can do what" questions in the system.

Hardcoded secret detection and prevention in git repositories and codebases using Gitleaks. Identifies passwords, API keys, tokens, and credentials through regex-based pattern matching and entropy analysis. Use when: (1) Scanning repositories for exposed secrets and credentials, (2) Implementing pre-commit hooks to prevent secret leakage, (3) Integrating secret detection into CI/CD pipelines, (4) Auditing codebases for compliance violations (PCI-DSS, SOC2, GDPR), (5) Establishing baseline sec...

Setup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI/Pydantic AI. Detects installed AI SDKs and configures appropriate integrations.

Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.

Reducing LLM token consumption in AI-assisted development sessions — RTK setup (CLI output compression via hook), session management (/clear, /compact, /rewind, named resumes), model tiering, and complementary habits. Use when setting up a new machine or project for agent work, when sessions are hitting context limits or rate limits too fast, when API costs are a concern, or whenever the user mentions tokens, context window, RTK, session cost, checkpoints, or resuming a session.